Computational modeling of the bacterial 70S ribosome and its

Computational modeling of the bacterial 70S ribosome and its

PRACE PRZEGL¥DOWE
Computational modeling of the
bacterial 70S ribosome and its
subunits
Joanna Trylska
Interdisciplinary Centre for Mathematical and Computational
Modelling, University of Warsaw, Warsaw
Computational modeling of the bacterial 70S ribosome and its subunits
Summary
The computational modeling studies performed on the entire 70S bacterial
ribosome and its subunits are reviewed. Computational approaches became
possible with the availability of three-dimensional atomic resolution structures
of the ribosomal subunits. However, due to enormous size of the system theoretical efforts to study the ribosome are few and still pose a great challenge. For
example, to extend the simulation time scales to biologically relevant ones, often reduced models requiring tedious parameterization procedures need to be
applied. So far modeling of the ribosome involved its electrostatic properties,
internal dynamics, binding of antibiotics, polypeptide folding in the ribosome
tunnel, and assembly paths of proteins in the small ribosomal subunit.
Address for correspondence
Joanna Trylska,
Interdisciplinary Centre
for Mathematical
and Computational
Modelling,
University of Warsaw,
Zwirki i Wigury St. 93,
02-089 Warsaw,
Poland;
e-mail:
[email protected]
1 (84) 36–47 2009
Key words:
70S ribosome, 30S subunit, molecular modeling, electrostatics, internal dynamics.
1. Introduction
Ribosome is a nanoscale ribonucleoprotein complex responsible for the process of translation in the cell. It is composed of
two subunits which in bacteria are denoted 30S and 50S in accord with their sedimentation coefficients. The two subunits associate through a network of interactions to form the 70S active
bacterial ribosome (see Fig.). The 50S subunit in bacteria conta-
Computational modeling of the bacterial 70S ribosome and its subunits
Fig. 70S T. thermophilus ribosome model based on the 1GIX and 1GIY crystal structures (5) and a homology model obtained in (13, 14). A: All-atomic model with proteins in magenta, ribosomal RNA in
blue, A-site tRNA in green, P-site tRNA in red, Mg2+ ions in yellow. B: All-atomic van der Waals model
with 30S subunit in yellow and 50S subunit in cyan. C: The electrostatic potential calculated for the
all-atom ribosome and projected onto its molecular surface. The scale changes from red Æ -5.0 kT/e,
through white 0 kT/e to blue æ +5.0 kT/e. D: Coarse-grained elastic network model of the ribosome. The
vertices denote P and Ca pseudo atoms and lines define the interactions.
ins over 30 proteins which are numbered and named beginning with the letter L to
indicate they are from the large subunit. It also contains two ribosomal RNA sequences called 23S rRNA (~2904 nucleotides) and 5S rRNA (~120 nucleotides). The 30S
subunit contains 21 proteins, a single 16S rRNA sequence (~1542 nucleotides), and
it binds messenger RNA (mRNA). Proteins in the small subunit are also numbered
and their names begin with the letter S.
Amino acids are brought to the ribosome by transfer RNAs (tRNAs). The ribosome comprises three tRNA binding sites located at the interface between subunits:
A – aminoacyl, P – peptidyl and E – exit. The A-site accepts the incoming aminoacylated tRNA, the P-site holds the tRNA with the emerging peptide chain, and the
E-site holds the deacylated tRNA before it leaves the ribosome. Peptide bond synthesis occurs between the aminoacyl and peptidyl ends of A-and P-site tRNAs in the
so called peptidyl transferase center in the large subunit. After the reaction, tRNAs
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Joanna Trylska
must cooperatively translocate to their new positions (P and E) with the advance of
mRNA by exactly one codon. The E-site tRNA is then ready to leave the ribosome
and a new tRNA may bind in the A-site. The nascent polypeptide leaves the ribosome through a tunnel in the 50S subunit.
The events occurring in three distinct stages of bacterial translation (initiation,
elongation, and termination) are modulated by the binding of various factors to the
ribosome-e.g., by elongation factor Tu (EF-Tu) and elongation factor G (EF-G) in the
elongation stage and release factors (RFs) in the termination stage. EF-Tu-GTP complexes with the aminoacyl-tRNA and delivers it to the A-site. When the appropriate
charged tRNA is deposited into the A-site, the GTP is hydrolyzed, the A-tRNA is accommodated, and the EF-Tu-GDP is released. Next, EF-G associates and promotes
the translocation of tRNAs and mRNA. The binding of these factors must be, therefore, very precise and must occur in exactly right phase of translation. Upon binding
elongation factors were found to undergo large structural rearrangements (1).
Peptide synthesis in bacteria can be inhibited by many antibiotics that bind to
specific sites (mainly RNA) in the ribosome (1). Some antibiotics prevent the association or dissociation of various factors, while others hinder the incorporation of
tRNAs, inhibit functional conformational changes of the ribosome or prevent the nascent peptide from leaving the ribosome. For example, aminoglycosidic antibiotics
bind to the 16S RNA in the 30S subunit and interfere with decoding. Bacteria develop resistance against various antibiotic classes; therefore computational modeling
also focuses on the aspects of antibiotic-ribosome binding.
With the appearance in the year 2000 of the all-atom crystal structures of the
30S (2,3) and 50S subunits (4) and a year later of a 5.5Å resolution structure of the
entire 70S ribosome (5), theoretical computational modeling of the ribosome became possible. This structural data deposited into the Protein Data Bank (6) provided
great opportunities to examine by various simulation techniques structural, dynamical and electrostatic properties of the ribosome. Nevertheless, from the point of
view of theoretical methods, modeling the entire ribosome or its subunits even
though feasible, is still challenging due to the system size. The 30S subunit is composed of approximately 95 000 atoms and spans a box of 210×180×200Å. The 50S
subunit is even larger and consists of approximately 140 000 atoms. Altogether the
bacterial ribosome has nearly 240 000 atoms not including the surrounding first-solvation
shell water molecules or counter-ions and its overall dimensions are about 25 nm3 .
2. Electrostatic properties of the ribosome
Electrostatics, due to its long-range nature, plays a key role in many biological
processes. Because biomolecules are characterized by complicated charge distributions, electrostatics often dominates their inter- and intra-molecular interactions.
Therefore, in theoretical studies of biomolecular properties, one of the important
38
PRACE PRZEGL¥DOWE
Computational modeling of the bacterial 70S ribosome and its subunits
aspects is a proper description of the electrostatic potential around molecules. The
most common and successful approach used to obtain such electrostatic properties
is the Poisson-Boltzmann theory (7,8). A molecule described by a set of fixed partial
charges and radii is immersed in implicit solvent with ionic strength. The ionic concentrations are described by the Boltzmann distribution. To obtain the electrostatic
potential the so called Poisson-Boltzmann equation is solved with numerical techniques on a three dimensional grid covering the molecule and its surrounding environment. The distance between the points of the grid defines the resolution of the
calculation.
The first effort to determine the electrostatic potential around the entire 30S subunit arose due to the development of the Adaptive Poisson-Boltzmann Solver (9)
(APBS). The software was designed to solve the Poisson-Boltzmann equation for large nanoscale systems. The authors calculated the electrostatic properties of all-atomic structures of the small and large ribosomal subunits. Calculations utilized 343
processors of the NPACI Blue Horizon supercomputer at San Diego Supercomputer
Center what resulted in a grid resolution of 0.4Å. The electrostatic potential around
the subunits was mainly negative due to the presence of negatively charged phosphate groups of RNA. Positive potential patches resulted from the ribosomal proteins and the presence of counterions. The potential surface maps between the subunit
association sides showed qualitative electrostatic complementarities. The authors
found that the codon binding sites are surrounded by positive potential regions
which could aid the stabilization of tRNAs and mRNA. The proteins of the large
subunit involved in tRNA binding also exhibit positive potential patches, especially
around L44e, L5 and L10e.
It was shown that during translation the ribosome undergoes large-scale conformational changes that are relevant to its function (10,11). One of these changes is
the ratchet-like movement of the subunits that takes place in the elongation stage
of translation. Therefore, the next electrostatic study explored the changes in the
electrostatic potential around the ribosome during the ratchet-like motion of the
subunits (12). The first 5.5Å resolution (5) crystal structure of the entire ribosome
was used and the conformations occurring during the ratcheting motion were derived from the normal mode analysis (see section Internal dynamics of the ribosome). Due to the availability of only low resolution structure of the 70S ribosome,
a reduced electrostatics Poisson-Boltzmann model was developed. Based on all-atom
electrostatic models of the 30S subunit effective partial charges and radii were parameterized and applied to the entire ribosome. The electrostatic potential around
the ribosome is mainly negative due to the fact that two-thirds of the ribosomal
mass arises from the RNA. However, positive patches of the electrostatic potential
are found and come mainly from the positively charged proteins. Changes in the
electrostatic potential along the ratchet-like displacement were observed mainly in
the tRNA binding sites. Positive electrostatic potential was observed in the areas
where the EF-G, EF-Tu and RFs bind. This study also showed the complementarities
BIOTECHNOLOGIA 1 (84) 36-47 2009
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Joanna Trylska
of the long-range electrostatic potential between EF-G and its binding site. The L1
protein was found to be a source of positive potential, possibly facilitating the release of E-site tRNA from the ribosome. The peptidyl transferase center which is bare
of proteins is characterized by a negative electrostatic potential. Large positive potential spots are associated mainly with proteins L6, L11, S12 and S19.
Recently all-atomic Poisson-Boltzmann calculations of the electrostatic potential
became possible. The figure presents the electrostatic potential projected on the
molecular surface of the entire 70S ribosome. Calculations were performed for the
model of Sanbonmatsu et al. (13,14) that was based on the crystal structure of Yusupov et al. (5). The positions of divalent ions corresponded to experimental conditions of 7 mM MgCl2 and were taken from the all-atom molecular dynamics simulations (14). The monovalent ionic strength was set to 150 mM. We also modeled the
missing side-chains of L1, L2, L7/L12, L9, L11, L13, L16, L23 and L25 proteins and
optimized their positions to avoid bad contacts. The all-atom derived electrostatic
potential confirms the earlier potential maps derived for the reduced ribosome model. Many strongly negative patches are observed even though positive areas corresponding mainly to proteins do appear.
3. Binding of antibiotics to the 30S subunit
Blocking the bacterial ribosome function leads to inhibition of microbial growth,
what is important in alleviating bacterial infections. Aminoglycoside antibiotics, positively charged sugar derivatives such as paromomycin and neamine, bind in the
A-site of the 30S subunit and interfere with the decoding. They have also been found to interfere with the assembly of ribosomal subunits (15). Aminoglycosides
were the first antibiotics to be crystallized in the complex with the 30S subunit, therefore, computational studies of their binding to the small subunit were initially
performed for this class of antibiotics.
The first theoretical study to determine the energetics of binding of aminoglycosides to the entire 30S subunit was performed by Ma et al. (16). Total binding free
energies were calculated based on the continuum solvation Poisson-Boltzmann model combined with the surface area-dependent nonpolar term and based on experimental data. The change in electrostatic term and the change in the solvent accessible surface area upon ligand binding to the 30S subunit were calculated to give the
binding energy of the ligand. This so called PB/SASA model is popular and serves
well in the studies of the energetics of binding (17). If a three dimensional structure
of a ligand-macromolecule complex is known from structural experiments, energetics of binding of the ligand may be studied. Therefore, when the first structure of the
30S subunit in complex with aminoglycosides was solved, computational energetic
studies were performed (16). The authors found reasonable correspondence with
experimental data obtained for binding of aminoglycosides to a model A-site RNA.
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Computational modeling of the bacterial 70S ribosome and its subunits
Later, Yang et al. (18) extended the previous model by additionally estimating
the entropy loss of aminoglycosidic rings upon binding, as well as the translational
and rotational entropy changes. The parameters of the model for the 30S subunit
were determined based on calculations for a smaller A-site oligonucleotide, as well
as experimental studies. These studies have shown that, despite many approximations, the implicit solvent Poisson-Boltzmann model is amenable to investigating
the overall binding hierarchy of antibiotics to the 30S subunit. Moreover, the model
was able to perceive small charge perturbations caused by different functional groups in the antibiotic rings.
By means of Brownian Dynamics, we have recently investigated the rates of binding of paromomycin to the whole 30S subunit (19). We found that the antibiotic
first diffuses toward the subunit and then explores the surface to encounter the
A-site. During the pathway leading to binding in the A-site it also interacts with
other possible cavities that are binding sites for other antibiotics.
4. Internal dynamics of the ribosome
By means of cryo-electron microscopy it was shown that the ribosome is a very
dynamic machine (10,11). For example, a ratchet-like motion of the subunits related
to translocation of tRNAs and mRNA was found (11). It was also postulated that
translocation involves a significant movement of the L1 stalk (20).
The global motions of the ribosome based on the 25Å cryo-electron microscopy
density maps and elastic network models were studied (21). The elastic net was constructed from the electron density map. The interactions were defined as harmonic
Hookean springs which means that they varied quadratically around the potential
energy minimum but it was found earlier that molecules often behave as if their
energy surface was parabolic. Normal mode analysis was applied to derive the lowest-frequency vibrational modes. The ratchet-like motion of the 30S relative to the
50S subunit was observed. This fact suggested that this movement is an inherent
property of the shape of the ribosome that can be treated as an elastic body. Global
motions depend on the global shape and character of deformations and not on the
details of the potential energy function and they often correspond to functional motions.
A more detailed study applied normal mode analysis to explore the internal dynamics of the ribosome based on the 5.5Å resolution crystal structure (22,23). In
this model only phosphate and Cd carbon atoms were considered. The system was
represented as an elastic network see (Fig.) and the lowest frequency modes were
characterized. It turned out that those modes include the ratchet-like relative motion of the subunits. Moreover, displacements of the L1 ribosomal stalk, opening
and closing the intersubunit space near the E-site was observed. The L11 protuberance on the other side of the ribosome also exhibited a large scale motion.
BIOTECHNOLOGIA 1 (84) 36-47 2009
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Joanna Trylska
In normal mode analysis harmonic potentials are applied and the motions can
only be characterized separately one at a time. Therefore, to overcome this
drawback and to increase the level of detail of the dynamics, a reduced molecular
dynamics simulation of the ribosome was performed, which accounted as well for
some anharmonicity of motions (12). The extension of the elastic network model
(Fig.) included all-atom like potential energy function, which distinguished both local bonded and non-local non-bonded interactions. The parameterization was based
on pair distribution functions, derived from the low-resolution crystal structure of
the ribosome (5). Half a microsecond molecular dynamics simulation time scales
were achieved. This study found that the movements of L7/L12 and L1 lateral stalks
are anti-correlated and span a range up to 15Å. Ratchet-like rotation of the subunits
correlated with the movement of the L1 stalk was also observed. The 50S subunit
was found to undergo many local internal fluctuations and the 30S subunit was
more compact and moving as one uniform block. The tRNA CCA-3’ termini and anticodons exhibited small fluctuations, which suggests tight alignment of substrates
during the formation of peptide bond and decoding.
The above reduced modeling of the internal dynamics of the ribosome has
proven that the ribosome internal mobility arises due to its shape and is inherent to
the system. Global functional motions and rearrangements are therefore only accelerated by GTP. It was found that translation is possible without GTP hydrolysis and
can indeed be factor-free (24,25), so the studies were in accord with experimental
data.
In the year 2005 Sanbonmatsu et al. (14) published the first all-atom molecular
dynamics simulation of the entire ribosome in explicit solvent, which focused on the
incorporation of tRNA into the ribosomal A-site. As the initial configuration, the
all-atom homology model of the Thermus thermophilus 70S ribosome was used (13),
with the entire simulation box consisting of 2.64 million atoms. Molecular dynamics
were performed at Los Alamos National Laboratory ASCI Q-machine supercomputer,
using 512, 768, and 1024 processors and about 106 computer hours. The overall
time scale of the simulation was about 20 ns. The authors simulated the accommodation of aminoacyl-tRNA with many 2ns long targeted molecular dynamics runs by
gradually reducing the root mean square deviation of the starting structure toward
the final A-tRNA configuration. Recently, all-atom molecular dynamics simulations of
the 30S ribosomal subunit based on the 1J5E structure including 1.07 million atoms
were also performed (26).
5. Ribosome tunnel
Before the release from the ribosome, the newly synthesized polypeptide travels
through a 100Å long tunnel in the 50S subunit. The lateral dimensions of the tunnel
are between 10 and 20Å and its walls are made mainly of ribosomal RNA. The tunnel
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Computational modeling of the bacterial 70S ribosome and its subunits
is believed to provide a constrained environment for co-translational folding, especially in case of a helices. An exemplary experimental study (27) found that the
structure formation can occur inside the ribosome but it can depend on the location
within the tunnel and the length of the nascent chain. It seems that it can also depend on the sequence and native structure of the protein.
Coarse-grained Langevin dynamics simulations of polypeptide folding in the tunnel modeled as a cylinder were performed (28). It was found that such confinement
can indeed entropically stabilize a-helices (28), because the diameter of the tunnel
is close to that of an a-helix. Longer helices were more stabilized than short ones.
Translocation of the b-hairpin-forming peptide through a tunnel that mimics the
one in the ribosome was also studied (29). The protein was represented in a coarse-grained manner and its movement via a cylindrical tunnel was also modeled with
Langevin dynamics. The authors have found, that when the diameter of the tunnel
decreases below a certain value of about 13.7Å, the protein assumes an extended
configuration. In a wider tunnel the protein is stabilized in the folded state.
However, Adrian Elcock (30), who studied the path of the peptide through the ribosome tunnel with a Go-like folding model (31), found that confinement of the
emerging polypeptide does not necessarily promote formation of the native structure during synthesis. Three model proteins were studied: chymotrypsin inhibitor 2,
barnase and Semliki forest virus protein. Amino acids were gradually added at the
ribosome peptidyl transfer center and according to Brownian dynamics protocol,
their movement through the large subunit tunnel was generated. The large subunit
was represented at a near-atomic level and the nascent proteins with only Ca carbons. Simple confinement of the protein within the tunnel did not significantly
affect its folding.
Therefore, more studies are needed to determine the influence of the ribosome
tunnel on the folding of synthesized polypeptides.
6. Assembly of the subunits
The assembly of the ribosome and its subunits is a complicated process that can
spontaneously occur under certain conditions also in vitro. This means that all the
information required for the proper assembly is present only in the RNA and protein
components. The assembly of the 30S subunit was found to be a sequential and cooperative process. The first experimental small subunit assembly map was determined by Held et al. (32). It classified the proteins into three groups: primary binders,
the ones that could bind to the bare 16S RNA; secondary binders – could bind only
after one of the primary proteins was bound; and tertiary proteins – could bind
only after at least one primary and at least one secondary protein were bound. In
1993 Powers et al. (33) presented a kinetics based 30S assembly map derived from
chemical foot-printing studies. The map classified proteins into early, middle,
BIOTECHNOLOGIA 1 (84) 36-47 2009
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Joanna Trylska
middle-late and late binders, and suggested that assembly proceeds roughly from
the 5’ to 3’ end of 16S RNA. The kinetics assembly landscape, predicting by
mass-spectrometry techniques the association rate constants for individual proteins, was also determined in (34). A nice review of the 30S subunit assembly biophysical studies is provided by James Williamson (35).
The 16S RNA can be divided into three individual domains: 5’, central, and 3’ and
the crystal structures have shown that majority of the 30S proteins interact exclusively with individual 16S RNA domains. The above experimental studies indicated
few pathways in the assembly process, corresponding to the 16S RNA domains, and
together with the 30S subunit crystal structures gave way to computational modeling studies of the assembly process.
First, Stagg et al. (36) explored the 3’ assembly path using coarse-grained Monte
Carlo simulated annealing technique. The one-bead per nucleotide and amino acid
model was applied with harmonic interactions for pseudo-bonds, angles and torsions, and a volume exclusion term (37). Morse-like potential was used to represent
specific protein-RNA interactions, which served as restraints to guide proteins to
their appropriate binding sites. Additional pseudo atoms were added between RNA
strands in helices to maintain their overall structure. The 16S rRNA starting model
contained information only on its secondary structure. The 30S subunit proteins
were sequentially added to the 16S RNA starting from a random position. Changes
of fluctuations upon binding of proteins in the 3’ domain (S7) path were assessed to
predict the contributions of each protein to the organization of the binding sites.
The authors found that the binding sites for primary binders are more ordered in
the bare 16S RNA than the sites for other proteins. Therefore, binding of primary
proteins reduces the flexibility of 16S rRNA and the primary binders also tend to organize the binding sites on RNA for later binders.
Another theoretical modeling of the 30S assembly map was based on the Poisson-Boltzmann model and calculations of the energetics of binding (12). The authors investigated the relative binding free energies of the 30S proteins to the 16S
RNA in various orders to determine the most probable assembly path based on the
energetics of binding. The electrostatic, nonpolar and entropic contributions to binding were taken into account. The Poisson-Boltzmann electrostatic energies were
calculated on 300 processors of NPACI Data Star or 800 processors of Blue Horizon
Supercomputers up to the 0.2Å grid resolution. The results demonstrated that for
majority of proteins electrostatics opposes binding, even though all except one of
the 30S proteins carries a positive net charge. This means that it is more favorable
for proteins to interact with the high dielectric solvent than with the ribosomal
RNA. Overall, the 5’ domain proteins that bear the highest positive charge get buried the most upon binding to 16S rRNA, and are the strongest binders. A computationally derived binding affinity map was constructed, which classified the proteins
according to the strength of interactions with the 16S rRNA and 30S subunit. Even
though the map was only qualitative and based on relative binding free energies it
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Computational modeling of the bacterial 70S ribosome and its subunits
proved the ability of the computational models to be applied not only to single proteins and small ligands, but also to molecular assemblies composed of proteins and
RNA.
To study the flexibility of 16S rRNA during binding of 30S proteins, Cui and Case
(38) employed a similar to Stagg et al. (36) coarse-grained RNA and protein model,
but used molecular dynamics technique. They performed multiple 100 ns long Langevin dynamics simulations and analyzed the conformational changes of 16S rRNA
in the bound and unbound forms. The 3’ domain i.e., the S7 assembly path was mainly studied, and the authors found structural explanations for the sequential binding of proteins in this path. The presence of primary and secondary proteins (S7
and S9) helps to organize the binding sites for tertiary proteins. Primary proteins
help in stabilizing 16S rRNA, whose configuration while bare of proteins deviates significantly from the crystal structure, and acquire an extended-like form. If a tertiary
protein is allowed to bind first, then the root mean square deviation of 16S rRNA is
similar as in its unbound form. In the protein-bound 16S rRNA, the central domain is
the most and the 3’ minor domain the least stable.
The dependence of proteins on each other’s binding to 16S rRNA was also studied with another reduced model (39). Each nucleotide and amino acid was represented as a single interacting center. The binding free energy was approximated by
harmonic interactions along the backbones and within a certain cutoff distance.
Bead-specific contact energy was also applied and the hamiltonian of the system
was integrated by an analytic method. The approach focused on both changes in
energetics and fluctuations. The interdependencies of protein binding were deduced by removing one or two proteins at a time from the full 30S complex. The binding free energies were computed as the difference between the energy of the complex and binding partners. The key proteins, most important in mutual stabilization
were identified, and the stabilizing effect of each protein on the remaining ones was
quantified. Non-local effects were found to be the cause of those influences. For
example, proteins S15, S16, S20 and peptide Thx were found not to influence the
binding of other proteins.
Recently, a similar dependence-based study was conducted (40), but was based
on the dynamical effects derived from normal mode analysis. The 30S system was
represented as an elastic network formed by Ca and P atoms connected by harmonic springs. The motions were analyzed based on anisotropic network model that
takes into account the directionality of fluctuations and their anisotropy. The authors removed single proteins, pairs of proteins and various sets of proteins from
the 30S complex and studied the effect of this removal on internal mobility of the
remaining part of the 30S subunit. It turned out, that the motions of the 16S rRNA
alone and inside the complete 30S subunit are very close. This means that the motion of the bare 16S rRNA is determined by its shape alone and that the intermolecular interactions between 16S rRNA and the 30S proteins do not affect the dynamics
of 16S rRNA in the complex. The normal modes also predicted that proteins S6 and
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Joanna Trylska
S18 behave as one block which is in accord with previous studies showing that these two proteins bind as a dimer (32,33,39).
Up to date, no similar studies on the 50S subunit were performed. The reason is
that the number of proteins in the 50S subunit is larger than in the 30S subunit and
at least over 30 proteins would need to be classified. Moreover, the overall net charge of the large subunit is around -3000e, which makes this subunit a more difficult
system to study with the known electrostatic models. The experimental approaches
to the 50S subunit assembly are reviewed in (41).
7. Conclusions
Thanks to high resolution structures of the ribosome, various modeling approaches gave insight into ribosome function and dynamics. Studies of its internal dynamics determined both, the global collective motions and analyzed the details of
the decoding process. Electrostatic calculations characterized the electrostatic potential around the ribosome. Various binding free energy techniques helped assess
the energetics and kinetics of binding of antibiotics to the 30S subunit. Assembly
paths of the 30S proteins were also derived. Despite the large size and high complexity of the ribosomal machine, all the molecular modeling efforts in the last years
were proven to be useful and corroborated experimental data. However, modeling
tools to be applicable to such nanoscale systems still need development. In future,
computational modeling should definitely focus not only on the prokaryotic but
also on the eukaryotic ribosomes, once atomic resolution structural data on the eukaryotic 80S ribosome become available.
Acknowledgements
The author acknowledges support from University of Warsaw (115/30/E-343/BST1345/ICM2008 and
G31-4), Polish Ministry of Science and Higher Education (3 T11F 005 30, 2006-2008), Fogarty International Center (NIH Grant no R03 TW07318) and Foundation for Polish Science.
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